Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x13e13917cf8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 5

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x13e139bd080>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if(not tf.test.gpu_device_name()):
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
C:\Continuum\Anaconda3\envs\TFLearn\lib\site-packages\ipykernel\__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    #Return the placeholders in the following order (tensor of real input images, tensor of z data, learning rate)
    return tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real'), \
           tf.placeholder(tf.float32, (None, z_dim), name='input_z'), \
           tf.placeholder(tf.float32, None, name='learning_rate')


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
alpha=0.1
stddev=0.02
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        layer_1 = tf.layers.conv2d(images, 32, 5, 2, padding='same', kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_1 = tf.maximum(layer_1 * alpha, layer_1) # 14x14x32
        
        layer_2 = tf.layers.conv2d(layer_1, 64, 5, 2, padding='same', kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_2 = tf.layers.batch_normalization(layer_2, training=True)
        layer_2 = tf.maximum(layer_2 * alpha, layer_2) # 7x7x64
        
        layer_3 = tf.layers.conv2d(layer_2, 128, 5, 2, padding='same',  kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_3 = tf.layers.batch_normalization(layer_3, training=True)
        layer_3 = tf.maximum(layer_3 * alpha, layer_3) # 4x4x128
        
        layer_4 = tf.layers.conv2d(layer_3, 256, 5, 2, padding='same', kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_4 = tf.layers.batch_normalization(layer_4, training=True)
        layer_4 = tf.maximum(layer_4 * alpha, layer_4) # 2x2x256
        
        flattened = tf.reshape(layer_4, (-1, 2*2*256))
        d_logits = tf.layers.dense(flattened, 1, kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        d_output = tf.sigmoid(d_logits)
        
        return d_output, d_logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        layer_1 = tf.layers.dense(z, 4 * 4 * 512)
        layer_1 = tf.reshape(layer_1, (-1, 4, 4, 512))
        layer_1 = tf.layers.batch_normalization(layer_1, training=is_train)
        layer_1 = tf.maximum(layer_1 * alpha, layer_1) # 4x4x512
        
        layer_2 = tf.layers.conv2d_transpose(layer_1, 128, 4, 1, padding='valid', kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_2 = tf.layers.batch_normalization(layer_2, training=is_train)
        layer_2 = tf.maximum(layer_2 * alpha, layer_2) # 8x8x128
        
        layer_3 = tf.layers.conv2d_transpose(layer_2, 64, 5, 2, padding='same', kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_3 = tf.layers.batch_normalization(layer_3, training=is_train)
        layer_3 = tf.maximum(layer_3 * alpha, layer_3) # 16x16x64
        
        layer_4 = tf.layers.conv2d_transpose(layer_3, 32, 5, 2, padding='same',kernel_initializer=tf.random_normal_initializer(stddev=stddev))
        layer_4 = tf.layers.batch_normalization(layer_4, training=is_train)
        layer_4 = tf.maximum(layer_4 * alpha, layer_4) # 32x32x32
        
        logits = tf.layers.conv2d_transpose(layer_4, out_channel_dim, 3, 1, padding='same', kernel_initializer=tf.random_normal_initializer(stddev=stddev))

        #activate the logits before returning the matching tensor
        return tf.tanh(logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    gen_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(gen_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * 0.9))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
    
    #return a tuple of (discriminator loss, generator loss)
    return d_loss_fake+d_loss_real, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    train_vars = tf.trainable_variables()
    #get all the trainable variables as succinct lists
    d_vars = [var for var in train_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in train_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    with tf.control_dependencies(gen_updates):
        d_train_ops = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_ops = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
        #return a tuple of (discriminator training operation, generator training operation).
        return d_train_ops, g_train_ops


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [14]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, width, height, depth = data_shape
    
    #Get various tensors for inputs, generator, and descriminator
    input_real, input_z, in_learning_rate = model_inputs(width, height, depth, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, depth)
    d_ops, g_ops = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    cnt = 0
    
    print_every = 10
    generate_rate = 100
    
    saver = tf.train.Saver()
    
    #standard training-session division - start session, iterate 'epoch' times, break into batches
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images *= 2
                cnt += 1
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                sess.run(d_ops, feed_dict={ input_real: batch_images, input_z: batch_z, in_learning_rate: learning_rate })
                sess.run(g_ops, feed_dict={ input_z: batch_z, in_learning_rate: learning_rate })
                
                if(cnt % print_every == 0):
                    d_loss_train = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    g_loss_train = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {0}/{1} - ".format(epoch_i+1, epoch_count),
                         "Discriminator Loss: {:.4f} - ".format(d_loss_train),
                         "Generator Loss: {:.4f}".format(g_loss_train))
                    
                if(cnt % generate_rate == 0):
                    show_generator_output(sess, 25, input_z, depth, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [15]:
batch_size = 64
z_dim = 128
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/3... Discriminator Loss: 0.7156... Generator Loss: 1.9557
Epoch 1/3... Discriminator Loss: 0.6698... Generator Loss: 1.8859
Epoch 1/3... Discriminator Loss: 0.3959... Generator Loss: 3.1910
Epoch 1/3... Discriminator Loss: 0.7266... Generator Loss: 1.3488
Epoch 1/3... Discriminator Loss: 0.4222... Generator Loss: 2.7714
Epoch 1/3... Discriminator Loss: 0.3618... Generator Loss: 3.5365
Epoch 1/3... Discriminator Loss: 0.5997... Generator Loss: 4.1700
Epoch 1/3... Discriminator Loss: 0.6505... Generator Loss: 1.9091
Epoch 1/3... Discriminator Loss: 0.8582... Generator Loss: 1.1310
Epoch 1/3... Discriminator Loss: 0.5318... Generator Loss: 2.1616
Epoch 1/3... Discriminator Loss: 1.0761... Generator Loss: 1.8213
Epoch 1/3... Discriminator Loss: 0.9238... Generator Loss: 1.7189
Epoch 1/3... Discriminator Loss: 0.7269... Generator Loss: 1.8640
Epoch 1/3... Discriminator Loss: 1.5296... Generator Loss: 0.4216
Epoch 1/3... Discriminator Loss: 0.6508... Generator Loss: 1.9551
Epoch 1/3... Discriminator Loss: 0.7008... Generator Loss: 2.4892
Epoch 1/3... Discriminator Loss: 0.9652... Generator Loss: 1.9558
Epoch 1/3... Discriminator Loss: 0.5930... Generator Loss: 2.0376
Epoch 1/3... Discriminator Loss: 1.5248... Generator Loss: 0.5292
Epoch 1/3... Discriminator Loss: 1.6073... Generator Loss: 0.4716
Epoch 1/3... Discriminator Loss: 1.0602... Generator Loss: 1.7726
Epoch 1/3... Discriminator Loss: 0.8253... Generator Loss: 1.3512
Epoch 1/3... Discriminator Loss: 0.9394... Generator Loss: 1.1583
Epoch 1/3... Discriminator Loss: 1.3319... Generator Loss: 0.5681
Epoch 1/3... Discriminator Loss: 1.1987... Generator Loss: 0.7406
Epoch 1/3... Discriminator Loss: 1.0038... Generator Loss: 1.3837
Epoch 1/3... Discriminator Loss: 0.9531... Generator Loss: 1.2370
Epoch 1/3... Discriminator Loss: 1.0118... Generator Loss: 0.9816
Epoch 1/3... Discriminator Loss: 1.1811... Generator Loss: 0.9996
Epoch 1/3... Discriminator Loss: 1.6170... Generator Loss: 0.4145
Epoch 1/3... Discriminator Loss: 1.0400... Generator Loss: 0.8714
Epoch 1/3... Discriminator Loss: 0.9131... Generator Loss: 1.3347
Epoch 1/3... Discriminator Loss: 0.7923... Generator Loss: 1.3921
Epoch 1/3... Discriminator Loss: 0.9590... Generator Loss: 1.3951
Epoch 1/3... Discriminator Loss: 0.7001... Generator Loss: 2.0923
Epoch 1/3... Discriminator Loss: 0.8174... Generator Loss: 1.3992
Epoch 1/3... Discriminator Loss: 1.2212... Generator Loss: 1.0731
Epoch 1/3... Discriminator Loss: 1.0130... Generator Loss: 1.1039
Epoch 1/3... Discriminator Loss: 1.2722... Generator Loss: 0.7773
Epoch 1/3... Discriminator Loss: 1.7106... Generator Loss: 0.4306
Epoch 1/3... Discriminator Loss: 1.1203... Generator Loss: 0.7585
Epoch 1/3... Discriminator Loss: 0.9414... Generator Loss: 1.0533
Epoch 1/3... Discriminator Loss: 1.0313... Generator Loss: 1.0392
Epoch 1/3... Discriminator Loss: 1.4296... Generator Loss: 0.4713
Epoch 1/3... Discriminator Loss: 0.5638... Generator Loss: 2.3290
Epoch 1/3... Discriminator Loss: 0.9553... Generator Loss: 1.0237
Epoch 1/3... Discriminator Loss: 1.0695... Generator Loss: 0.8457
Epoch 1/3... Discriminator Loss: 0.8456... Generator Loss: 1.1892
Epoch 1/3... Discriminator Loss: 1.1559... Generator Loss: 0.9074
Epoch 1/3... Discriminator Loss: 1.2631... Generator Loss: 0.7635
Epoch 1/3... Discriminator Loss: 1.5649... Generator Loss: 0.4948
Epoch 1/3... Discriminator Loss: 1.1911... Generator Loss: 0.6885
Epoch 1/3... Discriminator Loss: 1.1010... Generator Loss: 1.3530
Epoch 1/3... Discriminator Loss: 1.0389... Generator Loss: 1.1833
Epoch 1/3... Discriminator Loss: 0.9101... Generator Loss: 1.4418
Epoch 1/3... Discriminator Loss: 1.4571... Generator Loss: 0.4663
Epoch 1/3... Discriminator Loss: 1.4690... Generator Loss: 0.5087
Epoch 1/3... Discriminator Loss: 1.6565... Generator Loss: 0.3473
Epoch 1/3... Discriminator Loss: 1.2067... Generator Loss: 1.1245
Epoch 1/3... Discriminator Loss: 1.0382... Generator Loss: 0.8401
Epoch 1/3... Discriminator Loss: 1.0164... Generator Loss: 0.8330
Epoch 1/3... Discriminator Loss: 0.9035... Generator Loss: 1.2679
Epoch 1/3... Discriminator Loss: 1.2706... Generator Loss: 0.6545
Epoch 1/3... Discriminator Loss: 0.7333... Generator Loss: 1.5440
Epoch 1/3... Discriminator Loss: 1.2246... Generator Loss: 0.7940
Epoch 1/3... Discriminator Loss: 1.1486... Generator Loss: 0.6665
Epoch 1/3... Discriminator Loss: 0.9897... Generator Loss: 1.0619
Epoch 1/3... Discriminator Loss: 1.0842... Generator Loss: 0.8495
Epoch 1/3... Discriminator Loss: 1.0516... Generator Loss: 1.4089
Epoch 1/3... Discriminator Loss: 1.4439... Generator Loss: 0.4794
Epoch 1/3... Discriminator Loss: 1.0714... Generator Loss: 1.0720
Epoch 1/3... Discriminator Loss: 1.0077... Generator Loss: 1.8720
Epoch 1/3... Discriminator Loss: 1.6477... Generator Loss: 0.3750
Epoch 1/3... Discriminator Loss: 0.9713... Generator Loss: 1.0751
Epoch 1/3... Discriminator Loss: 0.7980... Generator Loss: 1.1304
Epoch 1/3... Discriminator Loss: 1.3982... Generator Loss: 0.5149
Epoch 1/3... Discriminator Loss: 1.4180... Generator Loss: 0.5465
Epoch 1/3... Discriminator Loss: 1.2895... Generator Loss: 0.5884
Epoch 1/3... Discriminator Loss: 1.1882... Generator Loss: 0.6977
Epoch 1/3... Discriminator Loss: 1.3152... Generator Loss: 0.5529
Epoch 1/3... Discriminator Loss: 1.2283... Generator Loss: 0.6022
Epoch 1/3... Discriminator Loss: 0.9252... Generator Loss: 1.1140
Epoch 1/3... Discriminator Loss: 1.7957... Generator Loss: 2.4193
Epoch 1/3... Discriminator Loss: 0.8073... Generator Loss: 1.3780
Epoch 1/3... Discriminator Loss: 0.8269... Generator Loss: 1.2668
Epoch 1/3... Discriminator Loss: 1.3728... Generator Loss: 0.5819
Epoch 1/3... Discriminator Loss: 1.2906... Generator Loss: 0.5795
Epoch 1/3... Discriminator Loss: 1.1693... Generator Loss: 1.2677
Epoch 1/3... Discriminator Loss: 1.2236... Generator Loss: 0.6154
Epoch 1/3... Discriminator Loss: 0.8883... Generator Loss: 1.1314
Epoch 1/3... Discriminator Loss: 1.1084... Generator Loss: 0.7077
Epoch 1/3... Discriminator Loss: 1.0687... Generator Loss: 0.7124
Epoch 1/3... Discriminator Loss: 1.3535... Generator Loss: 1.0794
Epoch 2/3... Discriminator Loss: 0.9842... Generator Loss: 0.8851
Epoch 2/3... Discriminator Loss: 1.1838... Generator Loss: 2.1875
Epoch 2/3... Discriminator Loss: 1.1419... Generator Loss: 0.9647
Epoch 2/3... Discriminator Loss: 1.1427... Generator Loss: 0.6525
Epoch 2/3... Discriminator Loss: 1.0304... Generator Loss: 1.0695
Epoch 2/3... Discriminator Loss: 2.4192... Generator Loss: 0.1602
Epoch 2/3... Discriminator Loss: 1.2481... Generator Loss: 1.4986
Epoch 2/3... Discriminator Loss: 1.4119... Generator Loss: 0.6738
Epoch 2/3... Discriminator Loss: 1.2290... Generator Loss: 0.6301
Epoch 2/3... Discriminator Loss: 0.9882... Generator Loss: 1.0170
Epoch 2/3... Discriminator Loss: 2.0511... Generator Loss: 0.2227
Epoch 2/3... Discriminator Loss: 0.9820... Generator Loss: 1.2834
Epoch 2/3... Discriminator Loss: 1.5645... Generator Loss: 0.4611
Epoch 2/3... Discriminator Loss: 1.5084... Generator Loss: 0.4183
Epoch 2/3... Discriminator Loss: 2.0963... Generator Loss: 0.2098
Epoch 2/3... Discriminator Loss: 1.4937... Generator Loss: 0.4746
Epoch 2/3... Discriminator Loss: 0.7594... Generator Loss: 1.4099
Epoch 2/3... Discriminator Loss: 0.7214... Generator Loss: 1.3863
Epoch 2/3... Discriminator Loss: 0.7720... Generator Loss: 1.9019
Epoch 2/3... Discriminator Loss: 1.2375... Generator Loss: 0.6159
Epoch 2/3... Discriminator Loss: 1.3081... Generator Loss: 0.5483
Epoch 2/3... Discriminator Loss: 1.7979... Generator Loss: 0.4379
Epoch 2/3... Discriminator Loss: 1.3589... Generator Loss: 0.9228
Epoch 2/3... Discriminator Loss: 0.8366... Generator Loss: 1.1693
Epoch 2/3... Discriminator Loss: 1.7510... Generator Loss: 0.3196
Epoch 2/3... Discriminator Loss: 1.0661... Generator Loss: 0.9260
Epoch 2/3... Discriminator Loss: 2.1338... Generator Loss: 0.2220
Epoch 2/3... Discriminator Loss: 0.9273... Generator Loss: 1.1272
Epoch 2/3... Discriminator Loss: 0.9590... Generator Loss: 1.1178
Epoch 2/3... Discriminator Loss: 1.1145... Generator Loss: 1.5256
Epoch 2/3... Discriminator Loss: 1.8768... Generator Loss: 0.2834
Epoch 2/3... Discriminator Loss: 1.1679... Generator Loss: 0.8306
Epoch 2/3... Discriminator Loss: 0.9268... Generator Loss: 1.1908
Epoch 2/3... Discriminator Loss: 2.6697... Generator Loss: 0.1295
Epoch 2/3... Discriminator Loss: 0.9399... Generator Loss: 1.4283
Epoch 2/3... Discriminator Loss: 1.0916... Generator Loss: 0.6971
Epoch 2/3... Discriminator Loss: 0.7579... Generator Loss: 1.4480
Epoch 2/3... Discriminator Loss: 0.9463... Generator Loss: 0.8877
Epoch 2/3... Discriminator Loss: 1.8195... Generator Loss: 0.3306
Epoch 2/3... Discriminator Loss: 1.1593... Generator Loss: 0.7384
Epoch 2/3... Discriminator Loss: 1.4549... Generator Loss: 0.4898
Epoch 2/3... Discriminator Loss: 1.5080... Generator Loss: 0.5011
Epoch 2/3... Discriminator Loss: 1.7215... Generator Loss: 0.3354
Epoch 2/3... Discriminator Loss: 0.9436... Generator Loss: 1.1381
Epoch 2/3... Discriminator Loss: 0.8073... Generator Loss: 1.1453
Epoch 2/3... Discriminator Loss: 0.9421... Generator Loss: 1.0397
Epoch 2/3... Discriminator Loss: 0.9484... Generator Loss: 0.8854
Epoch 2/3... Discriminator Loss: 2.5621... Generator Loss: 0.1446
Epoch 2/3... Discriminator Loss: 1.3617... Generator Loss: 0.5083
Epoch 2/3... Discriminator Loss: 2.1342... Generator Loss: 0.2067
Epoch 2/3... Discriminator Loss: 1.0690... Generator Loss: 0.7572
Epoch 2/3... Discriminator Loss: 0.9004... Generator Loss: 2.2274
Epoch 2/3... Discriminator Loss: 1.3018... Generator Loss: 0.5593
Epoch 2/3... Discriminator Loss: 1.7272... Generator Loss: 0.3171
Epoch 2/3... Discriminator Loss: 1.2096... Generator Loss: 1.2322
Epoch 2/3... Discriminator Loss: 0.8808... Generator Loss: 1.4005
Epoch 2/3... Discriminator Loss: 1.4844... Generator Loss: 0.4690
Epoch 2/3... Discriminator Loss: 0.8472... Generator Loss: 1.0169
Epoch 2/3... Discriminator Loss: 2.0789... Generator Loss: 0.2154
Epoch 2/3... Discriminator Loss: 0.7879... Generator Loss: 1.8469
Epoch 2/3... Discriminator Loss: 1.0732... Generator Loss: 0.8089
Epoch 2/3... Discriminator Loss: 0.8863... Generator Loss: 0.9898
Epoch 2/3... Discriminator Loss: 0.9249... Generator Loss: 0.9661
Epoch 2/3... Discriminator Loss: 2.1087... Generator Loss: 0.2121
Epoch 2/3... Discriminator Loss: 0.8512... Generator Loss: 1.3770
Epoch 2/3... Discriminator Loss: 2.2890... Generator Loss: 0.1957
Epoch 2/3... Discriminator Loss: 1.0054... Generator Loss: 0.8475
Epoch 2/3... Discriminator Loss: 1.4338... Generator Loss: 0.4702
Epoch 2/3... Discriminator Loss: 0.9404... Generator Loss: 0.9014
Epoch 2/3... Discriminator Loss: 1.1864... Generator Loss: 1.8970
Epoch 2/3... Discriminator Loss: 1.1996... Generator Loss: 0.6564
Epoch 2/3... Discriminator Loss: 1.0809... Generator Loss: 0.7197
Epoch 2/3... Discriminator Loss: 1.5608... Generator Loss: 0.3922
Epoch 2/3... Discriminator Loss: 0.7854... Generator Loss: 1.1465
Epoch 2/3... Discriminator Loss: 0.9966... Generator Loss: 1.7208
Epoch 2/3... Discriminator Loss: 1.0325... Generator Loss: 0.7732
Epoch 2/3... Discriminator Loss: 2.2163... Generator Loss: 0.2109
Epoch 2/3... Discriminator Loss: 1.3592... Generator Loss: 0.5533
Epoch 2/3... Discriminator Loss: 1.6072... Generator Loss: 0.3849
Epoch 2/3... Discriminator Loss: 0.8911... Generator Loss: 1.1826
Epoch 2/3... Discriminator Loss: 2.2394... Generator Loss: 0.1899
Epoch 2/3... Discriminator Loss: 1.1728... Generator Loss: 0.7065
Epoch 2/3... Discriminator Loss: 0.8677... Generator Loss: 1.4935
Epoch 2/3... Discriminator Loss: 0.9495... Generator Loss: 2.0386
Epoch 2/3... Discriminator Loss: 0.8775... Generator Loss: 1.1137
Epoch 2/3... Discriminator Loss: 0.6629... Generator Loss: 1.6597
Epoch 2/3... Discriminator Loss: 1.3613... Generator Loss: 0.5664
Epoch 2/3... Discriminator Loss: 1.1824... Generator Loss: 1.2383
Epoch 2/3... Discriminator Loss: 1.3208... Generator Loss: 0.5216
Epoch 2/3... Discriminator Loss: 1.0230... Generator Loss: 0.8938
Epoch 2/3... Discriminator Loss: 1.6508... Generator Loss: 0.3909
Epoch 2/3... Discriminator Loss: 1.0201... Generator Loss: 1.0702
Epoch 2/3... Discriminator Loss: 1.4912... Generator Loss: 0.4252
Epoch 2/3... Discriminator Loss: 1.6698... Generator Loss: 0.3544
Epoch 3/3... Discriminator Loss: 1.4850... Generator Loss: 0.8707
Epoch 3/3... Discriminator Loss: 1.3389... Generator Loss: 0.5747
Epoch 3/3... Discriminator Loss: 1.2705... Generator Loss: 0.6558
Epoch 3/3... Discriminator Loss: 1.0505... Generator Loss: 0.8857
Epoch 3/3... Discriminator Loss: 1.7829... Generator Loss: 3.5714
Epoch 3/3... Discriminator Loss: 1.5335... Generator Loss: 0.4109
Epoch 3/3... Discriminator Loss: 1.6440... Generator Loss: 0.3606
Epoch 3/3... Discriminator Loss: 1.9974... Generator Loss: 0.2293
Epoch 3/3... Discriminator Loss: 1.3305... Generator Loss: 0.7311
Epoch 3/3... Discriminator Loss: 2.4224... Generator Loss: 0.1582
Epoch 3/3... Discriminator Loss: 0.8242... Generator Loss: 1.0686
Epoch 3/3... Discriminator Loss: 1.3232... Generator Loss: 0.5275
Epoch 3/3... Discriminator Loss: 1.3737... Generator Loss: 0.5229
Epoch 3/3... Discriminator Loss: 0.8618... Generator Loss: 2.0557
Epoch 3/3... Discriminator Loss: 1.3316... Generator Loss: 0.5383
Epoch 3/3... Discriminator Loss: 1.4528... Generator Loss: 0.5735
Epoch 3/3... Discriminator Loss: 1.2568... Generator Loss: 0.5853
Epoch 3/3... Discriminator Loss: 0.8358... Generator Loss: 1.0696
Epoch 3/3... Discriminator Loss: 1.0973... Generator Loss: 1.4522
Epoch 3/3... Discriminator Loss: 1.6249... Generator Loss: 0.3754
Epoch 3/3... Discriminator Loss: 1.8611... Generator Loss: 0.2982
Epoch 3/3... Discriminator Loss: 1.4222... Generator Loss: 0.4814
Epoch 3/3... Discriminator Loss: 0.7134... Generator Loss: 1.2606
Epoch 3/3... Discriminator Loss: 1.6290... Generator Loss: 0.3796
Epoch 3/3... Discriminator Loss: 2.3979... Generator Loss: 0.1639
Epoch 3/3... Discriminator Loss: 1.3813... Generator Loss: 0.5087
Epoch 3/3... Discriminator Loss: 1.3591... Generator Loss: 0.6141
Epoch 3/3... Discriminator Loss: 0.9478... Generator Loss: 1.1800
Epoch 3/3... Discriminator Loss: 1.5593... Generator Loss: 0.4325
Epoch 3/3... Discriminator Loss: 1.2746... Generator Loss: 0.7765
Epoch 3/3... Discriminator Loss: 1.5714... Generator Loss: 0.4090
Epoch 3/3... Discriminator Loss: 1.0085... Generator Loss: 1.5781
Epoch 3/3... Discriminator Loss: 1.7814... Generator Loss: 0.3434
Epoch 3/3... Discriminator Loss: 1.9678... Generator Loss: 0.2418
Epoch 3/3... Discriminator Loss: 1.1742... Generator Loss: 0.6486
Epoch 3/3... Discriminator Loss: 1.6993... Generator Loss: 0.3426
Epoch 3/3... Discriminator Loss: 1.1966... Generator Loss: 0.7606
Epoch 3/3... Discriminator Loss: 1.3157... Generator Loss: 0.5376
Epoch 3/3... Discriminator Loss: 1.8138... Generator Loss: 0.3791
Epoch 3/3... Discriminator Loss: 1.3211... Generator Loss: 0.6367
Epoch 3/3... Discriminator Loss: 1.2764... Generator Loss: 0.5854
Epoch 3/3... Discriminator Loss: 0.9470... Generator Loss: 0.9727
Epoch 3/3... Discriminator Loss: 1.8797... Generator Loss: 0.2711
Epoch 3/3... Discriminator Loss: 1.4096... Generator Loss: 0.4784
Epoch 3/3... Discriminator Loss: 1.4098... Generator Loss: 0.5601
Epoch 3/3... Discriminator Loss: 1.2001... Generator Loss: 0.6235
Epoch 3/3... Discriminator Loss: 1.3500... Generator Loss: 1.0930
Epoch 3/3... Discriminator Loss: 1.7400... Generator Loss: 0.3255
Epoch 3/3... Discriminator Loss: 1.0258... Generator Loss: 0.7856
Epoch 3/3... Discriminator Loss: 1.1412... Generator Loss: 0.7472
Epoch 3/3... Discriminator Loss: 1.4856... Generator Loss: 0.4641
Epoch 3/3... Discriminator Loss: 1.3740... Generator Loss: 0.5295
Epoch 3/3... Discriminator Loss: 1.5209... Generator Loss: 0.4203
Epoch 3/3... Discriminator Loss: 0.9951... Generator Loss: 0.8455
Epoch 3/3... Discriminator Loss: 0.8654... Generator Loss: 1.1866
Epoch 3/3... Discriminator Loss: 1.4866... Generator Loss: 0.5213
Epoch 3/3... Discriminator Loss: 0.9727... Generator Loss: 0.9401
Epoch 3/3... Discriminator Loss: 1.2881... Generator Loss: 1.0729
Epoch 3/3... Discriminator Loss: 1.9574... Generator Loss: 0.2553
Epoch 3/3... Discriminator Loss: 1.0104... Generator Loss: 1.0304
Epoch 3/3... Discriminator Loss: 1.5918... Generator Loss: 0.3817
Epoch 3/3... Discriminator Loss: 0.8275... Generator Loss: 1.2700
Epoch 3/3... Discriminator Loss: 1.1341... Generator Loss: 0.7257
Epoch 3/3... Discriminator Loss: 1.7341... Generator Loss: 0.3989
Epoch 3/3... Discriminator Loss: 0.8810... Generator Loss: 0.9726
Epoch 3/3... Discriminator Loss: 0.8851... Generator Loss: 1.4158
Epoch 3/3... Discriminator Loss: 0.8709... Generator Loss: 1.0583
Epoch 3/3... Discriminator Loss: 1.8512... Generator Loss: 0.2851
Epoch 3/3... Discriminator Loss: 1.2110... Generator Loss: 0.7045
Epoch 3/3... Discriminator Loss: 0.7117... Generator Loss: 1.4442
Epoch 3/3... Discriminator Loss: 1.4548... Generator Loss: 0.4689
Epoch 3/3... Discriminator Loss: 0.9717... Generator Loss: 1.0902
Epoch 3/3... Discriminator Loss: 1.2355... Generator Loss: 0.6671
Epoch 3/3... Discriminator Loss: 1.0519... Generator Loss: 0.8575
Epoch 3/3... Discriminator Loss: 1.7371... Generator Loss: 0.3586
Epoch 3/3... Discriminator Loss: 1.2714... Generator Loss: 0.5841
Epoch 3/3... Discriminator Loss: 1.4200... Generator Loss: 0.4959
Epoch 3/3... Discriminator Loss: 1.4195... Generator Loss: 0.5448
Epoch 3/3... Discriminator Loss: 1.3121... Generator Loss: 0.5531
Epoch 3/3... Discriminator Loss: 1.1736... Generator Loss: 0.6324
Epoch 3/3... Discriminator Loss: 2.2457... Generator Loss: 0.2188
Epoch 3/3... Discriminator Loss: 1.0647... Generator Loss: 0.7043
Epoch 3/3... Discriminator Loss: 1.1389... Generator Loss: 0.6556
Epoch 3/3... Discriminator Loss: 1.5604... Generator Loss: 0.4527
Epoch 3/3... Discriminator Loss: 1.3938... Generator Loss: 0.4757
Epoch 3/3... Discriminator Loss: 1.0460... Generator Loss: 0.9922
Epoch 3/3... Discriminator Loss: 1.3097... Generator Loss: 0.5809
Epoch 3/3... Discriminator Loss: 1.3155... Generator Loss: 0.5800
Epoch 3/3... Discriminator Loss: 1.0559... Generator Loss: 0.7178
Epoch 3/3... Discriminator Loss: 1.1288... Generator Loss: 1.6114
Epoch 3/3... Discriminator Loss: 1.1877... Generator Loss: 0.6584
Epoch 3/3... Discriminator Loss: 1.7071... Generator Loss: 0.3451
Epoch 3/3... Discriminator Loss: 1.1556... Generator Loss: 0.7122
Epoch 3/3... Discriminator Loss: 1.2800... Generator Loss: 0.5598

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [16]:
batch_size = 64
z_dim = 128
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.3776... Generator Loss: 0.4649
Epoch 1/1... Discriminator Loss: 1.5984... Generator Loss: 3.5711
Epoch 1/1... Discriminator Loss: 0.7537... Generator Loss: 2.5022
Epoch 1/1... Discriminator Loss: 0.9830... Generator Loss: 3.5179
Epoch 1/1... Discriminator Loss: 0.9710... Generator Loss: 1.2584
Epoch 1/1... Discriminator Loss: 0.7559... Generator Loss: 2.2670
Epoch 1/1... Discriminator Loss: 1.1806... Generator Loss: 1.0349
Epoch 1/1... Discriminator Loss: 1.0128... Generator Loss: 1.2321
Epoch 1/1... Discriminator Loss: 1.4950... Generator Loss: 0.5099
Epoch 1/1... Discriminator Loss: 1.3396... Generator Loss: 0.7414
Epoch 1/1... Discriminator Loss: 1.2628... Generator Loss: 2.9788
Epoch 1/1... Discriminator Loss: 0.8462... Generator Loss: 1.1073
Epoch 1/1... Discriminator Loss: 0.9404... Generator Loss: 0.9218
Epoch 1/1... Discriminator Loss: 1.0732... Generator Loss: 1.0876
Epoch 1/1... Discriminator Loss: 0.7491... Generator Loss: 1.6882
Epoch 1/1... Discriminator Loss: 1.0222... Generator Loss: 1.2301
Epoch 1/1... Discriminator Loss: 1.0171... Generator Loss: 1.2093
Epoch 1/1... Discriminator Loss: 1.1028... Generator Loss: 1.0460
Epoch 1/1... Discriminator Loss: 1.0382... Generator Loss: 1.2304
Epoch 1/1... Discriminator Loss: 1.1132... Generator Loss: 1.0100
Epoch 1/1... Discriminator Loss: 1.1708... Generator Loss: 1.0722
Epoch 1/1... Discriminator Loss: 1.2005... Generator Loss: 0.7536
Epoch 1/1... Discriminator Loss: 1.1134... Generator Loss: 1.0721
Epoch 1/1... Discriminator Loss: 1.0283... Generator Loss: 1.0469
Epoch 1/1... Discriminator Loss: 0.4909... Generator Loss: 2.2853
Epoch 1/1... Discriminator Loss: 0.7371... Generator Loss: 1.2923
Epoch 1/1... Discriminator Loss: 0.8385... Generator Loss: 1.2485
Epoch 1/1... Discriminator Loss: 1.2857... Generator Loss: 0.9069
Epoch 1/1... Discriminator Loss: 1.0632... Generator Loss: 1.0959
Epoch 1/1... Discriminator Loss: 1.5103... Generator Loss: 0.6302
Epoch 1/1... Discriminator Loss: 1.0766... Generator Loss: 0.8966
Epoch 1/1... Discriminator Loss: 0.7726... Generator Loss: 1.7568
Epoch 1/1... Discriminator Loss: 1.0033... Generator Loss: 1.0761
Epoch 1/1... Discriminator Loss: 0.9865... Generator Loss: 1.0992
Epoch 1/1... Discriminator Loss: 1.2181... Generator Loss: 0.6143
Epoch 1/1... Discriminator Loss: 1.3035... Generator Loss: 0.7507
Epoch 1/1... Discriminator Loss: 1.3215... Generator Loss: 0.7104
Epoch 1/1... Discriminator Loss: 1.2186... Generator Loss: 0.7133
Epoch 1/1... Discriminator Loss: 1.3709... Generator Loss: 0.5044
Epoch 1/1... Discriminator Loss: 1.8919... Generator Loss: 4.5794
Epoch 1/1... Discriminator Loss: 1.3263... Generator Loss: 1.1301
Epoch 1/1... Discriminator Loss: 0.7705... Generator Loss: 1.4699
Epoch 1/1... Discriminator Loss: 1.8438... Generator Loss: 0.3102
Epoch 1/1... Discriminator Loss: 1.2955... Generator Loss: 0.8130
Epoch 1/1... Discriminator Loss: 1.5776... Generator Loss: 0.4212
Epoch 1/1... Discriminator Loss: 1.1146... Generator Loss: 0.8607
Epoch 1/1... Discriminator Loss: 0.9292... Generator Loss: 1.0647
Epoch 1/1... Discriminator Loss: 1.4215... Generator Loss: 1.9746
Epoch 1/1... Discriminator Loss: 1.1406... Generator Loss: 0.7457
Epoch 1/1... Discriminator Loss: 0.7632... Generator Loss: 1.6887
Epoch 1/1... Discriminator Loss: 1.1753... Generator Loss: 0.7981
Epoch 1/1... Discriminator Loss: 1.0135... Generator Loss: 1.0458
Epoch 1/1... Discriminator Loss: 1.2002... Generator Loss: 0.7868
Epoch 1/1... Discriminator Loss: 0.8941... Generator Loss: 1.4272
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.6488
Epoch 1/1... Discriminator Loss: 2.2624... Generator Loss: 2.8643
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 1.9253
Epoch 1/1... Discriminator Loss: 0.7663... Generator Loss: 1.3566
Epoch 1/1... Discriminator Loss: 1.0865... Generator Loss: 0.7820
Epoch 1/1... Discriminator Loss: 0.9078... Generator Loss: 1.0853
Epoch 1/1... Discriminator Loss: 1.0448... Generator Loss: 0.8448
Epoch 1/1... Discriminator Loss: 1.5955... Generator Loss: 0.4781
Epoch 1/1... Discriminator Loss: 1.3576... Generator Loss: 0.5488
Epoch 1/1... Discriminator Loss: 1.2195... Generator Loss: 0.6475
Epoch 1/1... Discriminator Loss: 1.0447... Generator Loss: 1.3925
Epoch 1/1... Discriminator Loss: 0.7423... Generator Loss: 1.6701
Epoch 1/1... Discriminator Loss: 0.8422... Generator Loss: 1.3526
Epoch 1/1... Discriminator Loss: 1.8179... Generator Loss: 0.2938
Epoch 1/1... Discriminator Loss: 1.5643... Generator Loss: 0.4163
Epoch 1/1... Discriminator Loss: 1.1889... Generator Loss: 1.0277
Epoch 1/1... Discriminator Loss: 0.8894... Generator Loss: 1.2805
Epoch 1/1... Discriminator Loss: 0.8555... Generator Loss: 1.2612
Epoch 1/1... Discriminator Loss: 1.2509... Generator Loss: 0.6393
Epoch 1/1... Discriminator Loss: 1.3530... Generator Loss: 0.5534
Epoch 1/1... Discriminator Loss: 1.1745... Generator Loss: 0.6576
Epoch 1/1... Discriminator Loss: 0.8051... Generator Loss: 1.7803
Epoch 1/1... Discriminator Loss: 0.8613... Generator Loss: 1.7285
Epoch 1/1... Discriminator Loss: 0.9968... Generator Loss: 0.8953
Epoch 1/1... Discriminator Loss: 1.2824... Generator Loss: 0.6452
Epoch 1/1... Discriminator Loss: 1.2923... Generator Loss: 0.7617
Epoch 1/1... Discriminator Loss: 1.3517... Generator Loss: 1.8120
Epoch 1/1... Discriminator Loss: 1.4885... Generator Loss: 2.3008
Epoch 1/1... Discriminator Loss: 0.9639... Generator Loss: 1.3452
Epoch 1/1... Discriminator Loss: 1.3792... Generator Loss: 0.6507
Epoch 1/1... Discriminator Loss: 1.5254... Generator Loss: 0.4150
Epoch 1/1... Discriminator Loss: 0.8097... Generator Loss: 1.4333
Epoch 1/1... Discriminator Loss: 1.6587... Generator Loss: 0.3612
Epoch 1/1... Discriminator Loss: 1.0344... Generator Loss: 0.8020
Epoch 1/1... Discriminator Loss: 1.2203... Generator Loss: 2.9590
Epoch 1/1... Discriminator Loss: 1.5659... Generator Loss: 2.0178
Epoch 1/1... Discriminator Loss: 0.8396... Generator Loss: 1.8622
Epoch 1/1... Discriminator Loss: 0.9617... Generator Loss: 1.4184
Epoch 1/1... Discriminator Loss: 0.8163... Generator Loss: 1.0639
Epoch 1/1... Discriminator Loss: 1.0471... Generator Loss: 1.0686
Epoch 1/1... Discriminator Loss: 0.8475... Generator Loss: 1.3172
Epoch 1/1... Discriminator Loss: 1.8971... Generator Loss: 0.2848
Epoch 1/1... Discriminator Loss: 1.2024... Generator Loss: 1.0130
Epoch 1/1... Discriminator Loss: 0.8998... Generator Loss: 1.4759
Epoch 1/1... Discriminator Loss: 1.2258... Generator Loss: 1.3764
Epoch 1/1... Discriminator Loss: 0.9330... Generator Loss: 1.7463
Epoch 1/1... Discriminator Loss: 0.8241... Generator Loss: 2.0382
Epoch 1/1... Discriminator Loss: 1.5271... Generator Loss: 0.5717
Epoch 1/1... Discriminator Loss: 1.8680... Generator Loss: 0.2719
Epoch 1/1... Discriminator Loss: 1.2136... Generator Loss: 0.6676
Epoch 1/1... Discriminator Loss: 0.9227... Generator Loss: 0.9081
Epoch 1/1... Discriminator Loss: 1.6181... Generator Loss: 0.3771
Epoch 1/1... Discriminator Loss: 1.1681... Generator Loss: 2.3555
Epoch 1/1... Discriminator Loss: 1.0118... Generator Loss: 0.9444
Epoch 1/1... Discriminator Loss: 1.1485... Generator Loss: 1.7058
Epoch 1/1... Discriminator Loss: 1.4346... Generator Loss: 0.4624
Epoch 1/1... Discriminator Loss: 0.8987... Generator Loss: 1.9558
Epoch 1/1... Discriminator Loss: 0.9914... Generator Loss: 2.8115
Epoch 1/1... Discriminator Loss: 0.9735... Generator Loss: 1.0269
Epoch 1/1... Discriminator Loss: 1.1206... Generator Loss: 0.8070
Epoch 1/1... Discriminator Loss: 1.1091... Generator Loss: 1.3366
Epoch 1/1... Discriminator Loss: 0.9736... Generator Loss: 1.0705
Epoch 1/1... Discriminator Loss: 1.0281... Generator Loss: 1.2773
Epoch 1/1... Discriminator Loss: 0.9279... Generator Loss: 1.0499
Epoch 1/1... Discriminator Loss: 1.1492... Generator Loss: 0.8707
Epoch 1/1... Discriminator Loss: 0.9437... Generator Loss: 1.0448
Epoch 1/1... Discriminator Loss: 1.1912... Generator Loss: 1.7673
Epoch 1/1... Discriminator Loss: 1.2087... Generator Loss: 0.6147
Epoch 1/1... Discriminator Loss: 0.9734... Generator Loss: 1.4219
Epoch 1/1... Discriminator Loss: 1.2553... Generator Loss: 0.6562
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.5429
Epoch 1/1... Discriminator Loss: 0.8006... Generator Loss: 1.2033
Epoch 1/1... Discriminator Loss: 0.9847... Generator Loss: 1.1630
Epoch 1/1... Discriminator Loss: 0.7095... Generator Loss: 1.8670
Epoch 1/1... Discriminator Loss: 1.1144... Generator Loss: 2.7655
Epoch 1/1... Discriminator Loss: 1.0819... Generator Loss: 0.7788
Epoch 1/1... Discriminator Loss: 1.0958... Generator Loss: 1.8203
Epoch 1/1... Discriminator Loss: 0.9675... Generator Loss: 1.0847
Epoch 1/1... Discriminator Loss: 0.8630... Generator Loss: 1.5445
Epoch 1/1... Discriminator Loss: 1.1297... Generator Loss: 2.6412
Epoch 1/1... Discriminator Loss: 0.6307... Generator Loss: 2.2459
Epoch 1/1... Discriminator Loss: 0.7632... Generator Loss: 1.8043
Epoch 1/1... Discriminator Loss: 0.9970... Generator Loss: 0.8753
Epoch 1/1... Discriminator Loss: 1.0257... Generator Loss: 1.1996
Epoch 1/1... Discriminator Loss: 0.9978... Generator Loss: 1.3101
Epoch 1/1... Discriminator Loss: 1.1915... Generator Loss: 2.1393
Epoch 1/1... Discriminator Loss: 0.9402... Generator Loss: 1.2575
Epoch 1/1... Discriminator Loss: 1.0240... Generator Loss: 1.0791
Epoch 1/1... Discriminator Loss: 1.0385... Generator Loss: 0.7341
Epoch 1/1... Discriminator Loss: 0.9479... Generator Loss: 1.0471
Epoch 1/1... Discriminator Loss: 1.2836... Generator Loss: 2.1063
Epoch 1/1... Discriminator Loss: 1.4969... Generator Loss: 2.7932
Epoch 1/1... Discriminator Loss: 0.9618... Generator Loss: 1.3102
Epoch 1/1... Discriminator Loss: 1.4497... Generator Loss: 0.4445
Epoch 1/1... Discriminator Loss: 1.7120... Generator Loss: 0.3502
Epoch 1/1... Discriminator Loss: 0.8927... Generator Loss: 1.5048
Epoch 1/1... Discriminator Loss: 1.0333... Generator Loss: 1.1862
Epoch 1/1... Discriminator Loss: 2.2339... Generator Loss: 0.2071
Epoch 1/1... Discriminator Loss: 0.7903... Generator Loss: 2.0678
Epoch 1/1... Discriminator Loss: 1.4762... Generator Loss: 0.4639
Epoch 1/1... Discriminator Loss: 0.8927... Generator Loss: 0.9186
Epoch 1/1... Discriminator Loss: 1.0607... Generator Loss: 0.7149
Epoch 1/1... Discriminator Loss: 0.8667... Generator Loss: 1.1261
Epoch 1/1... Discriminator Loss: 1.0310... Generator Loss: 0.8254
Epoch 1/1... Discriminator Loss: 1.0407... Generator Loss: 1.1208
Epoch 1/1... Discriminator Loss: 1.1706... Generator Loss: 0.6722
Epoch 1/1... Discriminator Loss: 1.8420... Generator Loss: 4.1047
Epoch 1/1... Discriminator Loss: 0.8898... Generator Loss: 1.3870
Epoch 1/1... Discriminator Loss: 1.0602... Generator Loss: 0.7840
Epoch 1/1... Discriminator Loss: 1.4474... Generator Loss: 0.4441
Epoch 1/1... Discriminator Loss: 1.0238... Generator Loss: 0.7854
Epoch 1/1... Discriminator Loss: 0.7128... Generator Loss: 1.6743
Epoch 1/1... Discriminator Loss: 0.9286... Generator Loss: 1.1174
Epoch 1/1... Discriminator Loss: 1.0881... Generator Loss: 1.3252
Epoch 1/1... Discriminator Loss: 1.0275... Generator Loss: 0.9226
Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 3.2873
Epoch 1/1... Discriminator Loss: 1.1183... Generator Loss: 2.7378
Epoch 1/1... Discriminator Loss: 1.0786... Generator Loss: 2.2675
Epoch 1/1... Discriminator Loss: 0.8084... Generator Loss: 2.1518
Epoch 1/1... Discriminator Loss: 0.9602... Generator Loss: 0.8653
Epoch 1/1... Discriminator Loss: 0.6255... Generator Loss: 1.9039
Epoch 1/1... Discriminator Loss: 0.9167... Generator Loss: 0.9582
Epoch 1/1... Discriminator Loss: 0.8541... Generator Loss: 2.7714
Epoch 1/1... Discriminator Loss: 0.9253... Generator Loss: 0.9644
Epoch 1/1... Discriminator Loss: 1.5861... Generator Loss: 0.4267
Epoch 1/1... Discriminator Loss: 1.0768... Generator Loss: 0.8396
Epoch 1/1... Discriminator Loss: 1.2518... Generator Loss: 0.6350
Epoch 1/1... Discriminator Loss: 1.0746... Generator Loss: 1.6429
Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 0.6437
Epoch 1/1... Discriminator Loss: 1.4524... Generator Loss: 0.5083
Epoch 1/1... Discriminator Loss: 1.5854... Generator Loss: 0.3844
Epoch 1/1... Discriminator Loss: 1.0944... Generator Loss: 0.9103
Epoch 1/1... Discriminator Loss: 2.0534... Generator Loss: 0.2444
Epoch 1/1... Discriminator Loss: 0.8431... Generator Loss: 2.1435
Epoch 1/1... Discriminator Loss: 0.9475... Generator Loss: 1.1197
Epoch 1/1... Discriminator Loss: 0.9129... Generator Loss: 1.2603
Epoch 1/1... Discriminator Loss: 0.9571... Generator Loss: 1.3381
Epoch 1/1... Discriminator Loss: 0.8943... Generator Loss: 1.0678
Epoch 1/1... Discriminator Loss: 1.6088... Generator Loss: 0.3907
Epoch 1/1... Discriminator Loss: 1.0919... Generator Loss: 0.7522
Epoch 1/1... Discriminator Loss: 1.1036... Generator Loss: 2.7593
Epoch 1/1... Discriminator Loss: 0.8178... Generator Loss: 1.3033
Epoch 1/1... Discriminator Loss: 0.8840... Generator Loss: 1.1873
Epoch 1/1... Discriminator Loss: 1.0332... Generator Loss: 2.9846
Epoch 1/1... Discriminator Loss: 1.1329... Generator Loss: 0.9636
Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 0.6614
Epoch 1/1... Discriminator Loss: 1.1647... Generator Loss: 0.6929
Epoch 1/1... Discriminator Loss: 1.0436... Generator Loss: 1.1052
Epoch 1/1... Discriminator Loss: 0.9240... Generator Loss: 1.3672
Epoch 1/1... Discriminator Loss: 0.8385... Generator Loss: 1.7395
Epoch 1/1... Discriminator Loss: 0.9161... Generator Loss: 0.9196
Epoch 1/1... Discriminator Loss: 1.3892... Generator Loss: 0.5220
Epoch 1/1... Discriminator Loss: 1.1333... Generator Loss: 0.7324
Epoch 1/1... Discriminator Loss: 1.0122... Generator Loss: 1.7218
Epoch 1/1... Discriminator Loss: 1.2555... Generator Loss: 0.7045
Epoch 1/1... Discriminator Loss: 0.9710... Generator Loss: 0.8687
Epoch 1/1... Discriminator Loss: 1.5961... Generator Loss: 0.3751
Epoch 1/1... Discriminator Loss: 0.9673... Generator Loss: 1.4450
Epoch 1/1... Discriminator Loss: 0.9393... Generator Loss: 1.2697
Epoch 1/1... Discriminator Loss: 1.1102... Generator Loss: 0.9302
Epoch 1/1... Discriminator Loss: 1.2775... Generator Loss: 0.6006
Epoch 1/1... Discriminator Loss: 1.1649... Generator Loss: 0.6612
Epoch 1/1... Discriminator Loss: 1.1500... Generator Loss: 0.8854
Epoch 1/1... Discriminator Loss: 1.0541... Generator Loss: 1.7763
Epoch 1/1... Discriminator Loss: 1.2882... Generator Loss: 0.5909
Epoch 1/1... Discriminator Loss: 1.1008... Generator Loss: 2.6265
Epoch 1/1... Discriminator Loss: 0.9570... Generator Loss: 1.0462
Epoch 1/1... Discriminator Loss: 1.0849... Generator Loss: 1.1090
Epoch 1/1... Discriminator Loss: 0.9520... Generator Loss: 1.8315
Epoch 1/1... Discriminator Loss: 1.0255... Generator Loss: 0.9285
Epoch 1/1... Discriminator Loss: 1.2441... Generator Loss: 0.5958
Epoch 1/1... Discriminator Loss: 1.0426... Generator Loss: 0.8099
Epoch 1/1... Discriminator Loss: 1.2519... Generator Loss: 0.5691
Epoch 1/1... Discriminator Loss: 1.3001... Generator Loss: 0.6831
Epoch 1/1... Discriminator Loss: 1.1335... Generator Loss: 0.8600
Epoch 1/1... Discriminator Loss: 1.1096... Generator Loss: 1.5928
Epoch 1/1... Discriminator Loss: 1.0000... Generator Loss: 1.4154
Epoch 1/1... Discriminator Loss: 0.8862... Generator Loss: 1.1427
Epoch 1/1... Discriminator Loss: 1.3125... Generator Loss: 0.5836
Epoch 1/1... Discriminator Loss: 1.3154... Generator Loss: 0.6529
Epoch 1/1... Discriminator Loss: 1.0099... Generator Loss: 1.5024
Epoch 1/1... Discriminator Loss: 0.8249... Generator Loss: 1.2576
Epoch 1/1... Discriminator Loss: 0.9764... Generator Loss: 1.0245
Epoch 1/1... Discriminator Loss: 0.9885... Generator Loss: 1.6440
Epoch 1/1... Discriminator Loss: 1.0718... Generator Loss: 1.2275
Epoch 1/1... Discriminator Loss: 1.1381... Generator Loss: 0.9558
Epoch 1/1... Discriminator Loss: 0.8049... Generator Loss: 1.2975
Epoch 1/1... Discriminator Loss: 1.1281... Generator Loss: 0.7473
Epoch 1/1... Discriminator Loss: 1.1492... Generator Loss: 0.7389
Epoch 1/1... Discriminator Loss: 1.0037... Generator Loss: 1.8838
Epoch 1/1... Discriminator Loss: 0.8508... Generator Loss: 1.1137
Epoch 1/1... Discriminator Loss: 2.0438... Generator Loss: 0.2498
Epoch 1/1... Discriminator Loss: 1.3167... Generator Loss: 0.5921
Epoch 1/1... Discriminator Loss: 1.6371... Generator Loss: 0.3658
Epoch 1/1... Discriminator Loss: 0.8964... Generator Loss: 1.7949
Epoch 1/1... Discriminator Loss: 1.3434... Generator Loss: 0.5610
Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.5318
Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.5862
Epoch 1/1... Discriminator Loss: 0.9425... Generator Loss: 1.2322
Epoch 1/1... Discriminator Loss: 0.9846... Generator Loss: 1.3621
Epoch 1/1... Discriminator Loss: 1.0944... Generator Loss: 2.0059
Epoch 1/1... Discriminator Loss: 1.1165... Generator Loss: 0.9797
Epoch 1/1... Discriminator Loss: 1.4348... Generator Loss: 2.3777
Epoch 1/1... Discriminator Loss: 1.1942... Generator Loss: 0.7637
Epoch 1/1... Discriminator Loss: 1.2766... Generator Loss: 0.6040
Epoch 1/1... Discriminator Loss: 1.0159... Generator Loss: 0.8577
Epoch 1/1... Discriminator Loss: 1.1060... Generator Loss: 0.8933
Epoch 1/1... Discriminator Loss: 1.0652... Generator Loss: 0.8241
Epoch 1/1... Discriminator Loss: 1.0878... Generator Loss: 0.7930
Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.5577
Epoch 1/1... Discriminator Loss: 1.2191... Generator Loss: 1.3936
Epoch 1/1... Discriminator Loss: 1.4802... Generator Loss: 2.5227
Epoch 1/1... Discriminator Loss: 0.9502... Generator Loss: 0.9815
Epoch 1/1... Discriminator Loss: 0.9541... Generator Loss: 0.9477
Epoch 1/1... Discriminator Loss: 0.9950... Generator Loss: 1.1258
Epoch 1/1... Discriminator Loss: 1.0482... Generator Loss: 1.1129
Epoch 1/1... Discriminator Loss: 0.9789... Generator Loss: 1.2485
Epoch 1/1... Discriminator Loss: 1.3930... Generator Loss: 0.4832
Epoch 1/1... Discriminator Loss: 1.5904... Generator Loss: 0.3914
Epoch 1/1... Discriminator Loss: 0.9733... Generator Loss: 2.0675
Epoch 1/1... Discriminator Loss: 1.4839... Generator Loss: 0.4766
Epoch 1/1... Discriminator Loss: 1.3183... Generator Loss: 0.5765
Epoch 1/1... Discriminator Loss: 0.9543... Generator Loss: 1.6633
Epoch 1/1... Discriminator Loss: 1.0426... Generator Loss: 0.9645
Epoch 1/1... Discriminator Loss: 1.4710... Generator Loss: 0.5079
Epoch 1/1... Discriminator Loss: 1.3067... Generator Loss: 0.6070
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.5046
Epoch 1/1... Discriminator Loss: 1.2457... Generator Loss: 0.6164
Epoch 1/1... Discriminator Loss: 1.1811... Generator Loss: 0.7353
Epoch 1/1... Discriminator Loss: 1.3369... Generator Loss: 1.3765
Epoch 1/1... Discriminator Loss: 1.1299... Generator Loss: 0.8857
Epoch 1/1... Discriminator Loss: 1.0822... Generator Loss: 1.0425
Epoch 1/1... Discriminator Loss: 1.0157... Generator Loss: 0.9719
Epoch 1/1... Discriminator Loss: 1.0498... Generator Loss: 0.8824
Epoch 1/1... Discriminator Loss: 1.2057... Generator Loss: 2.2163
Epoch 1/1... Discriminator Loss: 1.0364... Generator Loss: 0.8023
Epoch 1/1... Discriminator Loss: 1.0083... Generator Loss: 1.3006
Epoch 1/1... Discriminator Loss: 1.3503... Generator Loss: 0.5880
Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 2.3046
Epoch 1/1... Discriminator Loss: 1.1614... Generator Loss: 0.7415
Epoch 1/1... Discriminator Loss: 1.0088... Generator Loss: 0.8155
Epoch 1/1... Discriminator Loss: 1.1353... Generator Loss: 1.5120
Epoch 1/1... Discriminator Loss: 1.2423... Generator Loss: 0.7322
Epoch 1/1... Discriminator Loss: 0.9482... Generator Loss: 1.0334
Epoch 1/1... Discriminator Loss: 1.0026... Generator Loss: 0.9020
Epoch 1/1... Discriminator Loss: 1.2508... Generator Loss: 0.6638
Epoch 1/1... Discriminator Loss: 1.4437... Generator Loss: 0.5070
Epoch 1/1... Discriminator Loss: 1.2357... Generator Loss: 0.6193
Epoch 1/1... Discriminator Loss: 0.9617... Generator Loss: 1.0027
Epoch 1/1... Discriminator Loss: 0.9628... Generator Loss: 1.1650
Epoch 1/1... Discriminator Loss: 1.0196... Generator Loss: 1.1650
Epoch 1/1... Discriminator Loss: 1.1527... Generator Loss: 1.3317
Epoch 1/1... Discriminator Loss: 0.6942... Generator Loss: 1.4183
Epoch 1/1... Discriminator Loss: 1.1692... Generator Loss: 0.7373
Epoch 1/1... Discriminator Loss: 1.0977... Generator Loss: 1.0122
Epoch 1/1... Discriminator Loss: 1.1281... Generator Loss: 0.7400
Epoch 1/1... Discriminator Loss: 1.5503... Generator Loss: 0.4270
Epoch 1/1... Discriminator Loss: 1.0891... Generator Loss: 1.3430
Epoch 1/1... Discriminator Loss: 1.1503... Generator Loss: 0.7510
Epoch 1/1... Discriminator Loss: 1.6855... Generator Loss: 0.3432
Epoch 1/1... Discriminator Loss: 1.1688... Generator Loss: 0.8293
Epoch 1/1... Discriminator Loss: 1.4330... Generator Loss: 0.5878

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.